Clustering is a commonly used machine learning model used to analyze a wide range of types of data that includes text data, image data, biological data, geographic data, etc. The Dirichlet process Gaussian mixture model (DPGMM) is a widely used nonparametric Bayesian model for clustering tasks in machine learning. The nonparametric feature in DPGMM is to use the Dirichlet process to support unknown components in the mixture which corresponds to the number of clusters in data rendering DPGMM a desirable tool in clustering for its efficacy and elegancy. However, due to its high modeling complexity, DPGMM has rarely been applied to cluster with relatively big datasets, e.g., datasets with more than a million of observations, due to the prohibitive clustering time incurred.